xychelsea/deepfacelab

DeepFaceLab/TensorFlow GPU-enabled Docker Container

Provides an https://hub.docker.com/r/nvidia/cuda container with https://github.com/iperov/DeepFaceLab pre-installed on an https://www.anaconda.com/ and https://hub.docker.com/r/tensorflow/tensorflow container xychelsea/tensorflow:latest-gpu.

DeepFaceLab with TensorFlow

https://github.com/iperov/DeepFaceLab is an open source research project, based on https://tensorflow.org exploring the role of machine learning as a tool in the creative process. https://tensorflow.org/ is an open source platform for machine learning. It provides tools, libraries and community resources for researcher and developers to build and deploy machine learning applications. https://anaconda.com/ is an open data science platform based on Python 3. This container installs TensorFlow through the conda command with a lightweight version of Anaconda (Miniconda) and the conda-forge https://conda-forge.org/ in the /usr/local/anaconda directory. The default user, anaconda runs a https://github.com/krallin/tini/ /usr/bin/tini, and comes preloaded with the conda command in the environment $PATH. Additional versions with https://hub.docker.com/r/nvidia/cuda/ support and https://jupyter.org/ tags are available.

NVIDIA/CUDA GPU-enabled Containers

Two flavors provide an https://hub.docker.com/r/nvidia/cuda container with https://tensorflow.org pre-installed through https://anaconda.com/.

Getting the containers

Vanilla DeepFaceLab

The base container, based on the xychelsea/tensorflow:latest from the https://hub.docker.com/r/xychelsea/anaconda3 (xychelsea/anaconda3:latest) running Tini shell. For the container with a /usr/bin/tini entry point, use:

bash
docker pull xychelsea/deepfacelab:latest

With Jupyter Notebooks server pre-installed, pull with:

bash
docker pull xychelsea/deepfacelab:latest-jupyter

DeepFaceLab with NVIDIA/CUDA GPU support

Modified versions of nvidia/cuda:latest container, with support for NVIDIA/CUDA graphical processing units through the Tini shell. For the container with a /usr/bin/tini entry point:

bash
docker pull xychelsea/deepfacelab:latest-gpu

With Jupyter Notebooks server pre-installed, pull with:

bash
docker pull xychelsea/deepfacelab:latest-gpu-jupyter

Running the containers

To run the containers with the generic Docker application or NVIDIA enabled Docker, use the docker run command with a bound volume directory workspace attached at mount point /usr/local/deepfacelab/workspace.

Vanilla DeepFaceLab

bash
docker run --rm -it \
    -v workspace:/usr/local/deepfacelab/workspace \
    xychelsea/deepfacelab:latest

With Jupyter Notebooks server pre-installed, run with:

bash
docker run --rm -it -d
     -v workspace:/usr/local/deepfacelab/workspace \
     -p 8888:8888 \
     xychelsea/deepfacelab:latest-jupyter

DeepFaceLab with NVIDIA/CUDA GPU support

bash
docker run --gpus all --rm -it
     -v workspace:/usr/local/deepface/workspace \
     xychelsea/deepfacelab:latest-gpu /bin/bash

With Jupyter Notebooks server pre-installed, run with:

bash
docker run --gpus all --rm -it -d
     -v workspace:/usr/local/deepfacelab/workspace \
     -p 8888:8888 \
     xychelsea/deepfacelab:latest-gpu-jupyter

Using DeepFaceLab

[TK]

Building the containers

To build either a GPU-enabled container or without GPUs, use the https://github.com/xychelsea/deepfacelab-docker GitHub repository.

bash
git clone git://github.com/iperov/DeepFaceLab.git

Vanilla DeepFaceLab

The base container, based on the xychelsea/deepfacelab:latest from the https://hub.docker.com/r/xychelsea/anaconda3 (xychelsea/anaconda3:latest) running Tini shell:

bash
docker build -t deepfacelab:latest -f Dockerfile .

With Jupyter Notebooks server pre-installed, build with:

bash
docker build -t deepfacelab:latest-jupyter -f Dockerfile.jupyter .

DeepFaceLab with NVIDIA/CUDA GPU support

bash
docker build -t deepfacelab:latest-gpu -f Dockerfile.nvidia .

With Jupyter Notebooks server pre-installed, build with:

docker build -t deepfacelab:latest-gpu-jupyter -f Dockerfile.nvidia-jupyter .

Environment

The default environment uses the following configurable options:

ANACONDA_GID=100
ANACONDA_PATH=/usr/local/anaconda3
ANACONDA_UID=1000
ANACONDA_USER=anaconda
ANACONDA_ENV=magenta
DEEPFACELAB_PATH=/usr/local/deepfacelab
DEEPFACELAB_HOME=$HOME/deepfacelab
DEEPFACELAB_WORKSPACE=$DEEPFACELAB_PATH/workspace
DEEPFACELAB_SCRIPTS=$DEEPFACELAB_PATH/scripts

References

• https://github.com/iperov/DeepFaceLab
• https://tensorflow.org
• https://hub.docker.com/r/nvidia/cuda
• https://www.anaconda.com/blog/tensorflow-in-anaconda
• https://conda-forge.org/